Deep learning approach for sentiment-based rating prediction using BERT and LSTM algorithm

Abstract

This study presents a technique for analysing customer reviews using BERT-BI LSTM for big data in the e-commerce industry. Learning textual characteristics and data are used to extract deeper vectors; first, the unlabelled text is modelled using the BERT model of training for the language presented in deep learning. Afterwards, the text data is used to provide the pre-training model. Secondly, in order to demonstrate the best textual characteristics, the BI LSTM framework is used to concurrently gather contextual information. With the BERT and Bi-LSTM models combined, we can better integrate the context for sentiment classification and improve the final feature vector quality for sentiment classification results. Lastly, we build a sentiment analysis model that corresponds to the consumer review content. Using the same dataset, this paper's suggested strategy was compared to three others in an experimentally-based comparison. With values reaching 92.64% for accuracy, 90.32% for recall, and 91.46% for F1-Measure, the findings show that the suggested strategy has the highest of these metrics.